import os
from langchain_core.prompts import PromptTemplate
from langchain.chains.question_answering import load_qa_chain
from langchain_community.output_parsers.rail_parser import GuardrailsOutputParser
from langchain_community.document_loaders import PyPDFLoader
import google.generativeai as genai
import gradio as gr

# Function for initialization
def initialize(pdf_file, question):
    try:
        # Access the uploaded file information from Gradio
        file_info = pdf_file

        # Check if a file was uploaded
        if file_info is not None:
            # Construct potential file path based on temporary directory and filename
            file_path = os.path.join("/tmp", file_info.name)  # Adjust temporary directory if needed
            if os.path.exists(file_path):
                # Process the PDF
                pdf_loader = PyPDFLoader(file_path)
                pages = pdf_loader.load_and_split()
                processed_context = "\n".join(str(page.page_content) for page in pages[:30])  # Limit to first 30 pages

                # Configure Google Generative AI (replace with your API key)
                genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))

                # Prompt template for formatting context and question
                prompt_template = """Answer the question as precise as possible using the provided context. If the answer is not contained in the context, say "answer not available in context" 

                Context: 
                {context}

                Question: 
                {question} 

                Answer:
                """

                prompt = PromptTemplate(template=prompt_template, input_variables=["context", "question"])

                # Generate answer using GeminiPro model
                # Load the GeminiPro model
                model = genai.GenerativeModel('gemini-pro')

                # Ensure your LLM object (model) inherits from Runnable and can generate text

                # Prepare the input data
                input_data = {
                    "context": processed_context,
                    "question": question
                }

                # Generate the answer using load_qa_chain
                stuff_chain = load_qa_chain(model, chain_type="stuff", prompt=prompt)
                stuff_answer = stuff_chain(input_data, return_only_outputs=True)

                # Extract the answer
                generated_answer = stuff_answer['output_text']

                return generated_answer
            else:
                return "Error: The uploaded file could not be found."
        else:
            return "Error: No PDF file was uploaded."

    except Exception as e:
        return f"An error occurred: {e}"  # Generic error handling

# Create a Gradio interface
interface = gr.Interface(
    fn=initialize,
    inputs=[
        gr.File(label="Upload PDF"),  # No need for 'type' argument
        gr.Textbox(label="Question")
    ],
    outputs="text",
    title="GeminiPro Q&A Bot",
    description="Ask questions about the uploaded PDF document.",
)

# Launch the interface
interface.launch()